Abstract
ABSTRACT Principal points of a distribution have been introduced by Flury [1] who tackled the problem of optimal grouping in multivariate data. In essence, principal points are the theoretical counterparts of cluster means obtained by a k-means clustering algorithm. There has been considerable effort to find efficient estimation procedures for principal points. It is well known that under certain conditions the k-means estimator is a consistent and asymptotically normal estimator of the population principal points. In this paper some material on principal points is reviewed and new algorithms for the estimation of principal points in univariate distributions (univariate principal points) are proposed. Additionally, the Bootstrap approach is applied to assess the variability of the suggested estimators.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Communications in Statistics - Simulation and Computation
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
